Due to the complex maritime navigation environment, Unmanned Surface Vessels (USVs) are influenced by unknown nonlinear dynamics arising from external disturbances and internal uncertainties. Achieving effective formation control while maintaining obstacle avoidance performance presents significant challenges. This article proposes a Neural Networks (NNs) adaptive formation Artificial Potential Field (APF) obstacle avoidance control method for multiple USVs. By employing online updates of Radial Basis Function (RBF) NNs technology, the unknown nonlinear dynamics are approximated, thus addressing complex nonlinear dynamics problems. In scenarios involving multiple USVs navigating under high wind and wave conditions, collisions with obstacles frequently occur. To tackle this issue, a leader-follower control strategy is designed that effectively addresses risk assessment and obstacle avoidance under such challenging conditions. Additionally, to account for saturation constraints or potential faults in the controller inputs commonly encountered in engineering applications, it implements an asymmetric auxiliary control system. Furthermore, the Lyapunov stability theorem is utilized to ensure the stability of both the formation control and obstacle avoidance algorithms for multiple USVs. Finally, the effectiveness of the proposed algorithm is validated through simulations.

An adaptive finite time trajectory tracking control method is presented for underactuated unmanned marine surface vessels (MSVs) by employing neural networks to approximate system uncertainties. The proposed algorithm is developed by combining event-triggered control (ETC) and finite-time convergence (FTC) techniques. The dynamic event-triggered condition is adopted to avert the frequent acting of actuators using an adjustable triggered variable to regulate the minimal inter-event times. While solving the system uncertainties and asymmetric input saturation, an adaptive neural networks based backstepping controller is designed based on FTC under bounded disturbances. In addition, via Lyapunov approach it is proved that all signals in the closed-loop system are semi-global uniformly ultimately bounded. Finally, simulations results are shown to demonstrate the effectiveness of this proposed scheme.

In the conditions of bad weather, the small vessels maneuvering ability of collision avoidance is seriously reduced and the vulnerability to maritime accidents increases due to the influence of strong wind and wave. This paper proposes a collision avoidance decision-making and control method for small vessels using the integrated collision risk assessment based on fuzzy logic. First, the speed of wind and height of wave in the bad weather conditions are fuzzified. Then, the collision risk indices are selected for fuzzy inference to get the basic collision risk which will be integrated with the vulnerability to collision accidents and it helps to improve the accuracy of collision risk assessment. According to the current multi-ships encounter situations, the similar cases are retrieved from the case database, and the collision avoidance decision is given after fuzzy adjustments. To implement collision avoidance actions, a fuzzy algorithm for nonlinear ship heading dynamic surface control (DSC) control considering input saturation and uncertainties is designed. The problem of "computational expansion" that may occur in high-order systems is avoided by using DSC technology. Finally, simulation experiments are implemented to verify the effectiveness of the collision avoidance method.

This research examines how false data injection attacks (FDIAs) impact the trajectory tracking control of underactuated surface vessels (USVs). The internal uncertain dynamics of the system are reconstructed using radial basis function neural networks (RBFNNs). In order to avoid the computational pressure of the RBFNNs on the system, the neural network weights, external disturbances, and FDIAs are converted into a single parameter learning form using the minimum learning parameters (MLPs). Next, a nonlinear feedback function is constructed and introduced into the controller design process, thereby avoiding the controller accuracy loss caused by MLPs. Within the backstepping method framework, the adaptive laws leverage deep information robust adaptive technology to estimate the upper limits of the uncertainty term. The closed-loop system is provided with a rigorous theoretical analysis by combining the Lyapunov stability theory. Finally, the effectiveness of the control scheme is verified by simulation. The results show that the proposed controller guarantees boundedness of all closed-loop signals and drives the tracking errors into a small neighborhood of the reference trajectory even under the attack of FDIAs and the influence of internal and external uncertainties.